Incorporating physics into ML algorithms involves supplementing an existing technique with additional biases.
Usually, ML practitioners take one of three approaches [1]}.
First, observational biases can be embedded through data augmentation techniques;
however, augmented data is not always available, and incorporating additional training
samples can become computationally expensive.
Second, physical constraints can be included by suitably penalising the loss function [2]}.
Third, inductive biases can be incorporated directly into
the machine learning architecture in the form of mathematical constraints [3]}.
As noted by [1]}, this third approach is arguably the most principled method
since it produces model that strictly satisfy the physical constraints.
piDMD falls into the final category.
